Anne-Sophie Pereira De Sá

Anyone who has spoken to me in the last few years knows how much I dislike CTR as a metric, and especially dislike how it is used to decide if an ad is good or not. I have put together some data and analysis in my latest PMW article which digs into this a bit further, which is available here: https://lnkd.in/ezz3F3BH There are some great metrics which can give us near real time feedback on creative impact, but none of them are click based. Sneak preview of some of these findings in the image below, but with much more context and analysis in the article. Let me know if you disagree! Thanks for Performance Marketing World for publishing this.

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Trust is hard to scale. It’s true in teams. It’s true in culture. And it’s true in AI. When we built Snowflake’s GTM AI Assistant on Snowflake Intelligence, the goal creating something sellers could rely on in the highest-stakes moments, a C-suite meeting, a renewal negotiation, a competitive pitch. That meant making design choices with trust at the center: - Better context beats more context. We curated only trusted, up-to-date content, not every document we could find. - Shared definitions before shared answers. We aligned on what metrics mean so AI doesn’t amplify business ambiguity. - Visible signals of accuracy. Verified query badges show when an answer is enterprise-grade. The impact isn’t just in 5,000+ weekly queries or hours saved. It’s in the confidence sellers will feel, knowing their AI assistant is grounded in the same trusted sources the whole organization stands behind. In both AI and culture, trust is built, choice by choice, design by design. Full story here: https://lnkd.in/ghZjjkGR

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📊 Self-service analytics: Every data team's dream... until reality hits. We all fantasize about stakeholders handling their own data needs while we focus on strategic work. But most democratization efforts fail spectacularly. The great team at Omni breaks down years of hard-earned lessons on what actually works. Spoiler alert: It's not plug-and-play, even with AI. Success comes from the perfect combination of people, process, and tools—in that exact order. Worth the read if you're tired of "self-service" projects that somehow create more work than before. #DataStrategy #Analytics #SelfService https://lnkd.in/dxWGfYtn

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𝙏𝙝𝙤𝙪𝙜𝙝𝙩 𝙛𝙤𝙧 𝙩𝙝𝙚 𝘿𝙖𝙮: As my team and I design and continue to build agentic AI–driven documentation supply chains, one truth has become undeniable: agentic AI demands intelligent, highly structured, semantically rich, self-describing content more than ever before. The real advantage lies not in simplifying authoring for humans - AI now performs most, if not all, of the document encoding - but in enriching our content with deep semantic structure. What matters is the intelligence needed to automate the entire content lifecycle - production, management, delivery - not the 10–15% of effort that goes into making it easier for humans to put words in an authoring tool.

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MIT report finds about 95% of enterprise gen-AI pilots show no measurable business impact. Only ~5% deliver value.  What’s working then? Let’s continue to learn from early winners in AI. One of the clearest is Intercom. By the numbers: Reaccelerated growth since the pivot (after 5 successive quarters of declining growth). Fin (AI first product) is in mid–eight figures today and tracking to $100M ARR in <3 quarters. Top of G2 for AI agents. Growing faster than most public software companies.   Learnings from the transition (conversation between Lenny and Eoghan McCabe, Founder and CEO, Intercom): 1. They went wartime early: “We need to become a wartime company. If we do not fight for this, we are dead.” CEO urgency set the tempo and removed optionality. 2. They focused on one lane: Intercom concentrated on customer service, killed side quests, and aligned product, marketing, and sales to one category. Focus created speed and a simple story. 3. They bet real dollars: Leadership committed roughly $100M to AI while cutting burn and canceling projects to fund the shift. Focus + frugality paid for the future. 4. They rewrote the culture: “I rewrote the values designed to be a sharp knife to cut out the parts of the company that I just knew wouldn’t be effective.” Over the period, ~40% of the company turned over. There was a soft coup attempt. Yet, 15–16 months later an internal survey showed 98–99% approval for leadership and strategy. 5. He owned the downside: McCabe recognized that if the AI bet failed, he should step down. Accountability enabled speed and conviction. 6. He admitted a change in vision: From an aversion to chatbots, McCabe’s philosophy shifted. He now argues a 24/7 expert agent that answers instantly can feel more personal than a 2–4 day wait for a canned reply. 7. They hired real AI talent: Intercom elevated a Chief AI Officer, empowered specialists, and brought in very young builders. Culture change matched the technical change. Again, not all of these will apply to your company given different contexts. It is already interesting to compare these with Shopify (external vs internal transformation, different size / growth profiles) Also: This summary does not do justice to the conversation. If you are intrigued, highly  recommend listening to the full conversation (thank you Eoghan McCabe and Lenny Rachitsky!). Links to the full episode, and the Shopify summary, in comments

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#LookerStudio CrUX Dashboard deprecation The Google Chrome team is deprecating the CrUX dashboard for a shutdown at the end of November. On this occasion, Barry Pollard wrote a very detailed article on the history of this project to provide Chrome technical performance data by site, its evolutions, as well as the possible alternatives for a migration that you must plan within the next 2 months if you are currently using the CrUX dashboard in Looker Studio. You have 2 options regarding this third-party data: 1️⃣ The "CrUX Vis" project 2️⃣ If you want to continue using the Looker Studio dashboard template: The underlying data, which was made easier to access in Looker Studio through the Chrome UX Report connector, has also been available via BigQuery for several years through a materialized view (chrome-ux-report.materialized.device_summary). You can query this view directly or ingest the desired data for the desired sites to store them in your warehouse against a query processing about 13 GB. The article includes a step-by-step tutorial to make this transition with a custom query. Up to you to choose the solution that best suits your needs...or start collecting first-party data in the meantime 😉 You will find the link to the article with the comments

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To celebrate the paperback edition launching next week, I’m counting down the main themes I’ve heard from audiences since publishing ‘AI Needs You’ last year. NUMBER 1 The single most frequent question? “Will AI rob us of what it means to be human?” This question gets to the heart of what’s at stake, and it’s why I wrote this book. My answer is that AI doesn't have the power to undermine our 'humanness' on its own. What it does is hold up a mirror to our values. Sometimes, I found that questions about AI weren’t actually questions about AI at all, but about the anxieties of our modern world. Questions about jobs, education, war – seemed to be about AI but I felt really just reflected uncertainties so many are experiencing right now. And this brings home the main theme of ‘AI Needs You’ - which is that, unless we actively work to shape it, AI will naturally reflect that world back to us. We often think of new technologies coming along and changing the way we live and work. But in fact it is just as true that new technologies are changed by the societal context in which they are built. That is the clear conclusion I share in ‘AI Needs You’, grounded in real examples from the past. At heart, AI is a deeply human endeavour. It is a choice, a set of decisions which can be made and unmade, according to the whims, values and politics of those making, enjoying and regulating it. And it is up to all of us to shape the future. To explore all of this further, and to read the new Preface included in the paperback edition, which sheds light on the past couple of crazy years in AI, follow the link in the comments below to pre-order ‘AI Needs You’ – out next week!

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Many of this year's conversations at Cannes Lions International Festival of Creativity highlighted the importance of data and AI in driving creativity and inspiring change in the advertising industry. At the same time, however, marketers have shifted their focus away from truly understanding consumers and are now treating them more as data points for building their AI models. And what about the role of AI in consumers' lives? How has AI changed their consumption of content, purchase behavior, and decision-making? 💡 In their search for quick and easy answers to their questions, consumers are increasingly using LLMs over traditional search engines. This means that it is now more important for marketers to think about 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐄𝐧𝐠𝐢𝐧𝐞 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 (GEO) vs traditional SEO. 💡 Consumers are increasingly 𝐫𝐞𝐥𝐢𝐚𝐧𝐭 𝐨𝐧 𝐀𝐈 𝐭𝐨 𝐜𝐮𝐫𝐚𝐭𝐞 𝐭𝐡𝐞𝐢𝐫 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧-𝐦𝐚𝐤𝐢𝐧𝐠 across a variety of categories. These categories range from ones traditionally associated with longer purchase cycles (e.g., financial services, education) to ones where they are open to fully delegate their decision-making to AI agents (e.g., CPG, travel). 💡 Consumers don't follow traditional moments of truth and are instead increasingly making their decisions based on the 𝐢𝐧𝐬𝐭𝐚𝐧𝐭 𝐦𝐨𝐦𝐞𝐧𝐭 𝐨𝐟 𝐭𝐫𝐮𝐭𝐡, where the whole funnel collapses from minutes, days, and months to sometimes mere seconds. 💡 Consumers are more likely 𝐭𝐨 𝐦𝐚𝐤𝐞 𝐮𝐧𝐩𝐥𝐚𝐧𝐧𝐞𝐝 𝐚𝐧𝐝 𝐢𝐦𝐩𝐮𝐥𝐬𝐞 𝐩𝐮𝐫𝐜𝐡𝐚𝐬𝐞𝐬, as with the amount of information they now have at hand, traditional purchase planning becomes extremely complex (due to both the amount of information and number of choices available). Hence, consumers are driven more by 𝐧𝐞𝐞𝐝 𝐬𝐭𝐚𝐭𝐞𝐬 rather than specific products and categories. Which consumer trends driven by the evolving role of AI do you observe in your categories? -- Thank you P&G Alumni Network - Official Page for the interesting conversations this summer! It's always inspiring to brainstorm and share perspectives with my amazing ex-P&G colleagues. ✨

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AI Delivers Returns - But Trust Decides Who Wins The latest SAS x IDC Data and AI Impact Report shows the ROI story is real - but it’s not coming from where you might expect. 📈 Productivity, customer experience, and growth top the list of value drivers 💸 Cost-cutting ranked last - efficiency alone isn’t the differentiator 🤖 Leaders say deterministic AI is more reliable - yet they trust generative AI more That’s the trust paradox defining this new phase. Early adopters are learning that it’s not enough for AI to work - people need to believe in how it works. The next competitive edge isn’t capability. It’s confidence. 👇 Do you think trust will become the biggest moat in enterprise AI? 🔗 Full report here: https://lnkd.in/gC8taJMM ******* Repost this to help your network & follow me Adam Graham for more content like this.

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💡 85% of AI projects fail—not because of weak models, but because of bad data. Before jumping into AI, ask yourself: 1️⃣ Is your data accessible across teams? 2️⃣ Is it trustworthy and governed? 3️⃣ Is it relevant to real business outcomes? 🚫 Without strong data foundations, even the best AI becomes noise. 📥 I’ve shared a practical framework to evaluate your org’s AI readiness. #AIReady #DataStrategy #AIGovernance #DigitalTransformation #EnterpriseAI #AILeadership #AIAdoption #DataQuality

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AI initiatives live and die in the gap between PowerPoint promises and actual GTM execution. Why are we still watching fancy AI demos without seeing any impact? Three common failure points I see in my conversations: 1. Teams launch (so many) tools that nobody actually uses 2. New AI toys aren't integrated into existing workflows 3. Someone bought the shiny new thing before figuring out what problem they're solving The pattern is painfully familiar: leadership team sees a demo, gets excited, drops cash, then six months later asks why they're not seeing ROI. Meanwhile the core sales enablement isn't even dialed in yet. Here's the reality check: Sustainable AI success requires the boring fundamentals. Map the process first. Find the actual friction. Then apply the right solution - which might be AI, or might just be better process design. Demos are easy. Implementation is hard. Adoption is everything. What AI tools are actually delivering value in your GTM stack?

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